遮挡人脸表情识别深度学习方法研究进展  

Research progress of deep learning methods for occlusion facial expression recognition

作　　者：南亚会 华庆一[1] Nan Yahui;Hua Qingyi(School Information Science&Technology,Northwest University,Xi’an 710127,China;Dept.of Computer Science&Technology,Lyuliang University,Lyuliang Shanxi 033000,China)

机构地区：[1]西北大学信息科学与技术学院,西安710127 [2]吕梁学院计算机科学与技术系,山西吕梁033000

出　　处：《计算机应用研究》2022年第2期321-330,共10页Application Research of Computers

摘　　要：在真实环境下遮挡是准确分析识别人脸表情的主要障碍之一。近年来研究者采用深度学习技术解决遮挡条件下表情误识别率高的问题。针对遮挡表情识别的深度学习算法和遮挡相关的问题进行归纳总结。首先,概括局部遮挡条件下表情识别的发展现状、表情的表示方式以及研究遮挡表情用到的数据集;其次,回顾遮挡表情识别深度学习方法的最新进展和分析遮挡对表情的影响;最后,总结主要技术挑战,研究难点及其可能的应对策略。目的是为将来的遮挡表情识别研究提供更有益的参考依据和基准。Occlusion in a real environment is one of the main obstacles to accurately analyze and recognize facial expressions.In recent years,researchers have used deep learning technology to solve the problem of high misrecognition rate of facial expressions under occlusion conditions.It mainly summarized the deep learning algorithm of occlusion facial expression recognition and occlusion-related issues.Firstly,this paper summarized the development status of facial expression recognition under partial occlusion conditions,expression representation,and the data set used to study occlusion expression.Secondly,it reviewed the occlusion expression identify the latest developments in deep learning methods and analyzed the impact of occlusion on expressions.Finally,it summarized the main technical challenges,research difficulties and possible coping strategies.The purpose is to provide a more useful reference and benchmark for future research on occlusion expression recognition.

关 键 词：人脸表情分析 表情识别 局部遮挡 深度学习 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]